@文章{信息}doi/10.2196/26307，作者=“Zhang, Lili and Vashisht, Himanshu and Nethra, Alekhya and Slattery, Brian and Ward, Tomas”，标题=“爱奥华赌博任务中慢性疼痛学习和持续行为特征的差异:基于网络的实验室研究”，期刊=“J Med Internet Res”，年=“2022”，月=“4”，日=“6”，卷=“24”，数=“4”，页=“e26307”，关键词=“慢性疼痛”;决策;计算模型;爱荷华赌博任务;背景:慢性疼痛是一个全球性的重大健康问题。据报道，患有慢性疼痛的人会经历决策障碍，但这些发现迄今为止都是基于传统的实验室实验。在这样的实验中，研究人员对条件有广泛的控制，可以更精确地消除潜在的混淆。相比之下，人们对慢性疼痛如何影响决策的了解要少得多，这些决策是通过实验室现场实验获得的。虽然这样的设置可能会引入更多的实验不确定性，但在更生态有效的背景下收集数据可以更好地表征慢性疼痛的现实影响。目的:我们旨在利用互联网技术和社交媒体，量化慢性疼痛患者和健康对照者在实验室现场环境中的决策差异。 Methods: A cross-sectional design with independent groups was used. A convenience sample of 45 participants was recruited through social media: 20 (44{\%}) participants who self-reported living with chronic pain, and 25 (56{\%}) people with no pain or who were living with pain for <6 months acting as controls. All participants completed a self-report questionnaire assessing their pain experiences and a neuropsychological task measuring their decision-making (ie, the Iowa Gambling Task) in their web browser at a time and location of their choice without supervision. Results: Standard behavioral analysis revealed no differences in learning strategies between the 2 groups, although qualitative differences could be observed in the learning curves. However, computational modeling revealed that individuals with chronic pain were quicker to update their behavior than healthy controls, which reflected their increased learning rate (95{\%} highest--posterior-density interval [HDI] 0.66-0.99) when fitted to the Values-Plus-Perseverance model. This result was further validated and extended on the Outcome-Representation Learning model as higher differences (95{\%} HDI 0.16-0.47) between the reward and punishment learning rates were observed when fitted to this model, indicating that individuals with chronic pain were more sensitive to rewards. It was also found that they were less persistent in their choices during the Iowa Gambling Task compared with controls, a fact reflected by their decreased outcome perseverance (95{\%} HDI −4.38 to −0.21) when fitted using the Outcome-Representation Learning model. Moreover, correlation analysis revealed that the estimated parameters had predictive value for the self-reported pain experiences, suggesting that the altered cognitive parameters could be potential candidates for inclusion in chronic pain assessments. Conclusions: We found that individuals with chronic pain were more driven by rewards and less consistent when making decisions in our laboratory-in-the-field experiment. In this case study, it was demonstrated that, compared with standard statistical summaries of behavioral performance, computational approaches offered superior ability to resolve, understand, and explain the differences in decision-making behavior in the context of chronic pain outside the laboratory. ", issn="1438-8871", doi="10.2196/26307", url="//www.mybigtv.com/2022/4/e26307", url="https://doi.org/10.2196/26307", url="http://www.ncbi.nlm.nih.gov/pubmed/35384855" }